Applying Neural Networks in Polygraph Testing
L. A. Derevyagin, V. V. Makarov, A. Yu. Molchanov, Владимир Иванович Цурков, A. N. Yakovlev · Journal of Computer and Systems Sciences International · 2022
Abstract We propose a machine learning approach to automate the polygraph examiner’s work with neural network architectures from the scikit-learn library based on the Voting Classification architecture and a transformer. This approach increases the efficiency of polygraph testing, provides matching by features, and decreases the number of erroneous conclusions on the testee’s answers. The following indicators (channels) are used: electrodermal resistance (galvanic skin response), blood vessel capacity (plethysmogram), and respiratory rhythms.